IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第2页

Energy-Efficient and Interpretable Multisensor Human Activity Recognition via Deep Fused Lasso Net 通过深度融合拉索网实现高能效、可解释的多传感器人类活动识别

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-23 DOI: 10.1109/TETCI.2024.3430008

Yu Zhou;Jingtao Xie;Xiao Zhang;Wenhui Wu;Sam Kwong

{"title":"Energy-Efficient and Interpretable Multisensor Human Activity Recognition via Deep Fused Lasso Net","authors":"Yu Zhou;Jingtao Xie;Xiao Zhang;Wenhui Wu;Sam Kwong","doi":"10.1109/TETCI.2024.3430008","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3430008","url":null,"abstract":"Utilizing data acquired by multiple wearable sensors can usually guarantee more accurate recognition for deep learning based human activity recognition. However, an increased number of sensors bring high processing cost, influencing real-time activity monitoring. Besides, existing methods rarely consider the interpretability of the recognition model in aspects of both the importance of the sensors and features, causing a gap between deep learning and their extendability in real-world scenario. In this paper, we cast the classical fused lasso model into a deep neural network, proposing a deep fused Lasso net (dfLasso-Net), which can perform sensor selection, feature selection and HAR in one end-to-end structure. Specifically, a two-level weight computing module (TLWCM) consisting of a senor weight net and a feature weight net is designed to measure the importance of sensors and features. In sensor weight net, spatial smoothness between physical channels within each sensor is considered to maximize the usage of selected sensors. And the feature weight net is able to maintain the physical meaning of the hand-crafted features through feature selection inside the sensors. By combining with the learning module for classification, HAR can be performed. We test dfLasso-Net on three multi-sensor based HAR datasets, demonstrating that dfLasso-Net achieves better recognition accuracy with the least number of sensors and provides good model interpretability by visualizing the weights of the sensors and features. Last but not least, dflasso-Net can be used as an effective filter-based feature selection approach with much flexibility.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3576-3588"},"PeriodicalIF":5.3,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information 电气和电子工程师学会《计算智能新课题论文集》出版信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-23 DOI: 10.1109/TETCI.2024.3427471

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors 电气和电子工程师学会《计算智能新课题论文集》（IEEE Transactions on Emerging Topics in Computational Intelligence）给作者的信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-23 DOI: 10.1109/TETCI.2024.3427475

引用次数: 0

Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis 基于交替方向乘法的自适应非负潜因分析法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-22 DOI: 10.1109/TETCI.2024.3420735

Yurong Zhong;Kechen Liu;Shangce Gao;Xin Luo

{"title":"Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis","authors":"Yurong Zhong;Kechen Liu;Shangce Gao;Xin Luo","doi":"10.1109/TETCI.2024.3420735","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3420735","url":null,"abstract":"Large scale interaction data are frequently found in industrial applications related with Big Data. Due to the fact that few interactions commonly happen among numerous nodes in real scenes, such data can be quantified into a High-Dimensional and Incomplete (HDI) matrix where most entries are unknown. An alternating-direction-method-based nonnegative latent factor model can perform efficient and accurate representation leaning to an HDI matrix, while its multiple hyper-parameters greatly limit its scalability for real applications. Aiming at implementing a highly-scalable and efficient latent factor model, this paper adopts the principle of particle swarm optimization and the tree-structured parzen estimator algorithm to facilitate the hyper-parameter adaptation mechanism, thereby building an Alternating-direction-method-based Adaptive Nonnegative Latent Factor (A\u0000<sup>2</sup>\u0000NLF) model. Its theoretical convergence is rigorously proved. Empirical studies on several nonnegative HDI matrices from real applications demonstrate that the proposed A\u0000<sup>2</sup>\u0000NLF model obtains higher computational and storage efficiency than several state-of-the-art models, along with significant accuracy gain. Its hyper-parameter adaptation is implemented smoothly, thereby greatly boosting its scalability in real problems.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3544-3558"},"PeriodicalIF":5.3,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Online Planning and Robust Optimal Control for Nonholonomic Mobile Robot in Unstructured Environments 非结构化环境下非全局性移动机器人的高效在线规划和鲁棒性优化控制

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-17 DOI: 10.1109/TETCI.2024.3424527

Yingbai Hu;Wei Zhou;Yueyue Liu;Minghao Zeng;Weiping Ding;Shu Li;Guoxin Li;Zheng Li;Alois Knoll

{"title":"Efficient Online Planning and Robust Optimal Control for Nonholonomic Mobile Robot in Unstructured Environments","authors":"Yingbai Hu;Wei Zhou;Yueyue Liu;Minghao Zeng;Weiping Ding;Shu Li;Guoxin Li;Zheng Li;Alois Knoll","doi":"10.1109/TETCI.2024.3424527","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3424527","url":null,"abstract":"In complex environments where occupied and unknown areas exceed the free space, it is essential for robots to utilize efficient methods for environmental perception, trajectory planning, and trajectory tracking. This paper introduces the jump point search (JPS) algorithm as a global planning approach and integrates the complete trajectory and safe trajectory using convex decomposition for local planning purposes. We specifically formulate the planning process as a jerk optimization problem to reduce robot vibrations and improve stability. To address trajectory tracking challenges, we propose an innovative robust control Lyapunov function method. This method efficiently manages disturbances in mobile robot motion, enhancing stability. It considers input constraints such as angular and linear velocity limits, along with optimization metrics like minimal input effort. We utilize a proximal augmented Lagrangian method to solve the optimization problem related to trajectory planning and the robust control Lyapunov function. Through experiments involving different friction forces and torques, we validate the effectiveness of our proposed robust control Lyapunov function controller in managing unknown disturbances. This demonstrates its superior adaptability and robustness compared to conventional model control.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3559-3575"},"PeriodicalIF":5.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Co-Occurrence Relationship Driven Hierarchical Attention Network for Brain CT Report Generation 用于生成脑 CT 报告的共现关系驱动的层次注意网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-06-18 DOI: 10.1109/TETCI.2024.3413002

Xiaodan Zhang;Shixin Dou;Junzhong Ji;Ying Liu;Zheng Wang

{"title":"Co-Occurrence Relationship Driven Hierarchical Attention Network for Brain CT Report Generation","authors":"Xiaodan Zhang;Shixin Dou;Junzhong Ji;Ying Liu;Zheng Wang","doi":"10.1109/TETCI.2024.3413002","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3413002","url":null,"abstract":"Automatic generation of medical reports for Brain Computed Tomography (CT) imaging is crucial for helping radiologists make more accurate clinical diagnoses efficiently. Brain CT imaging typically contains rich pathological information, including common pathologies that often co-occur in one report and rare pathologies that appear in medical reports with lower frequency. However, current research ignores the potential co-occurrence between common pathologies and pays insufficient attention to rare pathologies, severely restricting the accuracy and diversity of the generated medical reports. In this paper, we propose a Co-occurrence Relationship Driven Hierarchical Attention Network (CRHAN) to improve Brain CT report generation by mining common and rare pathologies in Brain CT imaging. Specifically, the proposed CRHAN follows a general encoder-decoder framework with two novel attention modules. In the encoder, a co-occurrence relationship guided semantic attention (CRSA) module is proposed to extract the critical semantic features by embedding the co-occurrence relationship of common pathologies into semantic attention. In the decoder, a common-rare topic driven visual attention (CRVA) module is proposed to fuse the common and rare semantic features as sentence topic vectors, and then guide the visual attention to capture important lesion features for medical report generation. Experiments on the Brain CT dataset demonstrate the effectiveness of the proposed method.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3643-3653"},"PeriodicalIF":5.3,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse Graph Tensor Learning for Multi-View Spectral Clustering 用于多视图光谱聚类的稀疏图张量学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-06-12 DOI: 10.1109/TETCI.2024.3409724

Man-Sheng Chen;Zhi-Yuan Li;Jia-Qi Lin;Chang-Dong Wang;Dong Huang

{"title":"Sparse Graph Tensor Learning for Multi-View Spectral Clustering","authors":"Man-Sheng Chen;Zhi-Yuan Li;Jia-Qi Lin;Chang-Dong Wang;Dong Huang","doi":"10.1109/TETCI.2024.3409724","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3409724","url":null,"abstract":"Multi-view spectral clustering has achieved impressive performance by learning multiple robust and meaningful similarity graphs for clustering. Generally, the existing literatures often construct multiple similarity graphs by certain similarity measure (e.g. the Euclidean distance), which lack the desired ability to learn sparse and reliable connections that carry critical information in graph learning while preserving the low-rank structure. Regarding the challenges, a novel Sparse Graph Tensor Learning for Multi-view Spectral Clustering (SGTL) method is designed in this paper, where multiple similarity graphs are seamlessly coupled with the cluster indicators and constrained with a low-rank graph tensor. Specifically, a novel graph learning paradigm is designed by establishing an explicit theoretical connection between the similarity matrices and the cluster indicator matrices, in order that the constructed similarity graphs enjoy the desired block diagonal and sparse property for learning a small portion of reliable links. Then, we stack multiple similarity matrices into a low-rank graph tensor to better preserve the low-rank structure of the reliable links in graph learning, where the key knowledge conveyed by singular values from different views is explicitly considered. Extensive experiments on several benchmark datasets demonstrate the superiority of SGTL.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3534-3543"},"PeriodicalIF":5.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bi-Search Evolutionary Algorithm for High-Dimensional Bi-Objective Feature Selection 用于高维双目标特征选择的双搜索进化算法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-30 DOI: 10.1109/TETCI.2024.3393388

Hang Xu;Bing Xue;Mengjie Zhang

{"title":"A Bi-Search Evolutionary Algorithm for High-Dimensional Bi-Objective Feature Selection","authors":"Hang Xu;Bing Xue;Mengjie Zhang","doi":"10.1109/TETCI.2024.3393388","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3393388","url":null,"abstract":"High dimensionality often challenges the efficiency and accuracy of a classifier, while evolutionary feature selection is an effective method for data preprocessing and dimensionality reduction. However, with the exponential expansion of search space along with the increase of features, traditional evolutionary feature selection methods could still find it difficult to search for optimal or near optimal solutions in the large-scale search space. To overcome the above issue, in this paper, we propose a bi-search evolutionary algorithm (termed BSEA) for tackling high-dimensional feature selection in classification, with two contradictory optimizing objectives (i.e., minimizing both selected features and classification errors). In BSEA, a bi-search evolutionary mode combining the forward and backward searching tasks is adopted to enhance the search ability in the large-scale search space; in addition, an adaptive feature analysis mechanism is also designed to the explore promising features for efficiently reproducing more diverse offspring. In the experiments, BSEA is comprehensively compared with 9 most recent or classic state-of-the-art MOEAs on a series of 11 high-dimensional datasets with no less than 2000 features. The empirical results suggest that BSEA generally performs the best on most of the datasets in terms of all performance metrics, along with high computational efficiency, while each of its essential components can take positive effect on boosting the search ability and together make the best contribution.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3489-3502"},"PeriodicalIF":5.3,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-modal Authentication Model for Occluded Faces in a Challenging Environment 挑战性环境中的隐蔽人脸多模式身份验证模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-30 DOI: 10.1109/TETCI.2024.3390058

Dahye Jeong;Eunbeen Choi;Hyeongjin Ahn;Ester Martinez-Martin;Eunil Park;Angel P. del Pobil

{"title":"Multi-modal Authentication Model for Occluded Faces in a Challenging Environment","authors":"Dahye Jeong;Eunbeen Choi;Hyeongjin Ahn;Ester Martinez-Martin;Eunil Park;Angel P. del Pobil","doi":"10.1109/TETCI.2024.3390058","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3390058","url":null,"abstract":"Authentication systems are crucial in the digital era, providing reliable protection of personal information. Most authentication systems rely on a single modality, such as the face, fingerprints, or password sensors. In the case of an authentication system based on a single modality, there is a problem in that the performance of the authentication is degraded when the information of the corresponding modality is covered. Especially, face identification does not work well due to the mask in a COVID-19 situation. In this paper, we focus on the multi-modality approach to improve the performance of occluded face identification. Multi-modal authentication systems are crucial in building a robust authentication system because they can compensate for the lack of modality in the uni-modal authentication system. In this light, we propose DemoID, a multi-modal authentication system based on face and voice for human identification in a challenging environment. Moreover, we build a demographic module to efficiently handle the demographic information of individual faces. The experimental results showed an accuracy of 99% when using all modalities and an overall improvement of 5.41%–10.77% relative to uni-modal face models. Furthermore, our model demonstrated the highest performance compared to existing multi-modal models and also showed promising results on the real-world dataset constructed for this study.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3463-3473"},"PeriodicalIF":5.3,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PV-SSD: A Multi-Modal Point Cloud 3D Object Detector Based on Projection Features and Voxel Features PV-SSD：基于投影特征和体素特征的多模态点云三维物体检测器

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-29 DOI: 10.1109/TETCI.2024.3389710

Yongxin Shao;Aihong Tan;Zhetao Sun;Enhui Zheng;Tianhong Yan;Peng Liao

{"title":"PV-SSD: A Multi-Modal Point Cloud 3D Object Detector Based on Projection Features and Voxel Features","authors":"Yongxin Shao;Aihong Tan;Zhetao Sun;Enhui Zheng;Tianhong Yan;Peng Liao","doi":"10.1109/TETCI.2024.3389710","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3389710","url":null,"abstract":"3D object detection using LiDAR is critical for autonomous driving. However, the point cloud data in autonomous driving scenarios is sparse. Converting the sparse point cloud into regular data representations (voxels or projection) often leads to information loss due to downsampling or excessive compression of feature information. This kind of information loss will adversely affect detection accuracy, especially for objects with fewer reflective points like cyclists. This paper proposes a multi-modal point cloud 3D object detector based on projection features and voxel features, which consists of two branches. One, called the voxel branch, is used to extract fine-grained local features. Another, called the projection branch, is used to extract projection features from a bird's-eye view and focus on the correlation of local features in the voxel branch. By feeding voxel features into the projection branch, we can compensate for the information loss in the projection branch while focusing on the correlation between neighboring local features in the voxel features. To achieve comprehensive feature fusion of voxel features and projection features, we propose a multi-modal feature fusion module (MSSFA). To further mitigate the loss of crucial features caused by downsampling, we propose a voxel feature extraction method (VR-VFE), which samples feature points based on their importance for the detection task. To validate the effectiveness of our method, we tested it on the KITTI dataset and ONCE dataset. The experimental results show that our method has achieved significant improvement in the detection accuracy of objects with fewer reflection points like cyclists.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3436-3449"},"PeriodicalIF":5.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0